Result for 286B8FC257073C08CC8ECD47ED66190E151B5A94

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize1056400
MD51D25980BD563B907017369A366ABCA27
SHA-1286B8FC257073C08CC8ECD47ED66190E151B5A94
SHA-2566F900A5CF7427ABB2ADE39117118570F3EA94049BF3CFD376F1F5615B76BB27C
SSDEEP24576:1E4gcSmbFRjzVcUrbtl8ERyfdV7ZmoVQ1ll6G6jnhD3ykL7dlPKNhLRxZmYLFqdt:1FFSmRRjzVcUrbtl8ER0dVpmLsGEhD3Z
TLSHT114253B87F09204ACD0ABF9705BB57953BA613858426C39762FA79D382B3EF116D1B703
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD52944CDDACEA549B00780E25D771B390C
PackageArchx86_64
PackageDescriptionCHOLMOD is a set of ANSI C routines for sparse Cholesky factorization and update/downdate. A MATLAB interface is provided. The performance of CHOLMOD was compared with 10 other codes in a paper by Nick Gould, Yifan Hu, and Jennifer Scott. see also their raw data. Comparing BCSLIB-EXT, CHOLMOD, MA57, MUMPS, Oblio, PARDISO, SPOOLES, SPRSBLKLLT, TAUCS, UMFPACK, and WSMP, on 87 large symmetric positive definite matrices, they found CHOLMOD to be fastest for 42 of the 87 matrices. Its run time is either fastest or within 10% of the fastest for 73 out of 87 matrices. Considering just the larger matrices, it is either the fastest or within 10% of the fastest for 40 out of 42 matrices. It uses the least amount of memory (or within 10% of the least) for 35 of the 42 larger matrices. Jennifer Scott and Yifan Hu also discuss the design considerations for a sparse direct code. CHOLMOD is part of the SuiteSparse sparse matrix suite.
PackageNamelibcholmod3
PackageRelease85.70
PackageVersion3.0.14
SHA-15626653D5C1A0A75359FA86E8CF2D7F58AECBD56
SHA-256196D889F864D0E0D7CC71A9F310A054020EE3A202D12CF59B64C7C4A64E400FE